Knopp, TobiasTobiasKnoppJürß, PaulPaulJürßGrosser, MircoMircoGrosser2023-04-182023-04-182023-03-19International Journal on Magnetic Particle Imaging 9 (1, suppl. 1): 2303008 (2023)http://hdl.handle.net/11420/15192Image reconstruction in magnetic particle imaging is a challenging task because the optimal image quality can only be obtained by tuning the reconstruction parameters for each measurement individually. In particular, it requires a proper selection of the Tikhonov regularization parameter. In this work we propose a deep-learning-based post-processing technique, which removes the need for manual parameter optimization. The proposed neural network takes several images reconstructed with different parameters as input and combines them into a single high-quality image.en2365-903320231, suppl. 1Infinite Science Publishinghttps://creativecommons.org/licenses/by/4.0/InformatikTechnikIngenieurwissenschaftenJournal Article10.15480/882.506410.18416/IJMPI.2023.230300810.15480/882.5064Journal Article